Graph collaborative filtering has made great progress in the recommender systems, but these methods often struggle with the data sparsity issue in real-world recommendation scenarios. To mitigate the effect of data sparsity, graph collaborative filtering incorporates contrastive learning as an auxiliary task to improve model performance. However, existing contrastive learning-based methods generally use a single data augmentation graph to construct the auxiliary contrastive learning task, which has problems such as loss of key information and low robustness. To address these problems, this paper proposes a Perturbation-driven Dual Auxiliary Contrastive Learning for Collaborative Filtering Recommendation (PDACL). PDACL designs structure perturbation and weight perturbation to construct two data augmentation graphs. The Structure Perturbation Augmentation (SPA) graph perturbs the topology of the user-item interaction graph, while the Weight Perturbation Augmentation (WPA) graph reconstructs the implicit feedback unweighted graph into a weighted graph similar to the explicit feedback. These two data augmentation graphs are combined with the user-item interaction graph to construct the dual auxiliary contrastive learning task to extract the self-supervised signals without losing key information and jointly optimize it together with the supervised recommendation task, to alleviate the data sparsity problem and improve the performance. Experimental results on multiple public datasets show that PDACL outperforms numerous benchmark models, demonstrating that the dual-perturbation data augmentation graph in PDACL can overcome the shortcomings of a single data augmentation graph, leading to superior recommendation results. The implementation of our work will be found at https://github.com/zky77/PDACL.